--------------------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  C:\Users\Dell\Dropbox\OutcomeUncertainty\II Replication Folder\Alliance Formation Log.log
  log type:  text
 opened on:  23 Sep 2017, 08:11:04

. clear

. use "Alliance Formation Data"

. *********************************************
. ******SUMMARY STATISTICS*********************
. *********************************************
. su  atopally0 W_SystemRELa   W_Regionalb   W_SystemRELaarep_MBallSW       arep_MBallSW aiis_bl S I jointenemy_dum sqrtdist mpc
> tdum poldif_using pol5_using

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
   atopally0 |  1,572,896    .0067665      .08198          0          1
W_SystemRELa |  1,315,954    .2401069    .0655075   .1887663   .5087313
 W_Regionalb |  1,315,954    .3999052    .0706209   .1899667   .6843948
W_System~lSW |  1,313,740    .0000521     .003383  -.0667389   .2334199
arep_MBallSW |  1,460,047    .0000386    .0115874   -.318352   .6110603
-------------+---------------------------------------------------------
     aiis_bl |  1,419,971    .0000148    .0029287  -.3333333    .676477
           S |  1,313,740    .7590874    .1889808   -.217391          1
           I |  1,288,912    .0001807    .0488406  -.9438869   .4209424
jointenemy~m |  1,582,576    .1043868    .3057617          0          1
    sqrtdist |  1,457,406    64.25661    23.73637          0   111.3328
-------------+---------------------------------------------------------
     mpctdum |  1,459,726     .102907    .3038375          0          1
poldif_using |  1,173,488    7.753124    6.313878          0         20
  pol5_using |  1,460,046    .1191647    .3239824          0          1

. 
. *********************************************
. ******TABLE 1******************************** - OK
. *********************************************
. 
. eststo clear

. eststo:  probit atopally0              arep_MBallSW aiis_bl S I jointenemy_dum sqrtdist mpctdum poldif_using pol5_using, cl(di
> rdyadID)

Iteration 0:   log pseudolikelihood = -42790.566  
Iteration 1:   log pseudolikelihood = -38104.031  
Iteration 2:   log pseudolikelihood = -36823.374  
Iteration 3:   log pseudolikelihood = -36816.321  
Iteration 4:   log pseudolikelihood = -36816.317  
Iteration 5:   log pseudolikelihood = -36816.317  

Probit regression                               Number of obs     =  1,045,707
                                                Wald chi2(9)      =   10226.42
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -36816.317               Pseudo R2         =     0.1396

                           (Std. Err. adjusted for 27,874 clusters in dirdyadID)
--------------------------------------------------------------------------------
               |               Robust
     atopally0 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
  arep_MBallSW |   1.440934   .1982492     7.27   0.000     1.052373    1.829495
       aiis_bl |   .6242837   .6098876     1.02   0.306    -.5710741    1.819642
             S |   .6206231   .0419362    14.80   0.000     .5384297    .7028166
             I |   .1985011   .0644611     3.08   0.002     .0721598    .3248425
jointenemy_dum |   .5483801   .0129736    42.27   0.000     .5229524    .5738078
      sqrtdist |  -.0118474   .0002035   -58.21   0.000    -.0122463   -.0114485
       mpctdum |   .1199821   .0145601     8.24   0.000     .0914449    .1485194
  poldif_using |   -.000911   .0008567    -1.06   0.288      -.00259    .0007681
    pol5_using |   .2127507   .0130766    16.27   0.000      .187121    .2383804
         _cons |  -2.539385   .0391937   -64.79   0.000    -2.616203   -2.462567
--------------------------------------------------------------------------------
(est1 stored)

. eststo:  probit atopally0 W_SystemRELa arep_MBallSW aiis_bl S I jointenemy_dum sqrtdist mpctdum poldif_using pol5_using, cl(di
> rdyadID)

Iteration 0:   log pseudolikelihood = -42790.566  
Iteration 1:   log pseudolikelihood = -37844.773  
Iteration 2:   log pseudolikelihood = -36495.364  
Iteration 3:   log pseudolikelihood = -36485.388  
Iteration 4:   log pseudolikelihood = -36485.379  
Iteration 5:   log pseudolikelihood = -36485.379  

Probit regression                               Number of obs     =  1,045,707
                                                Wald chi2(10)     =   10932.72
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -36485.379               Pseudo R2         =     0.1473

                           (Std. Err. adjusted for 27,874 clusters in dirdyadID)
--------------------------------------------------------------------------------
               |               Robust
     atopally0 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
  W_SystemRELa |  -2.051094   .0863023   -23.77   0.000    -2.220244   -1.881945
  arep_MBallSW |   1.713341   .1991716     8.60   0.000     1.322972    2.103711
       aiis_bl |   .7586221   .6296862     1.20   0.228    -.4755401    1.992784
             S |   .6399346   .0405546    15.78   0.000     .5604489    .7194202
             I |    .168354   .0620262     2.71   0.007     .0467849    .2899231
jointenemy_dum |   .5542143   .0132693    41.77   0.000     .5282071    .5802216
      sqrtdist |  -.0123165    .000207   -59.50   0.000    -.0127222   -.0119108
       mpctdum |   .2100753   .0154515    13.60   0.000      .179791    .2403597
  poldif_using |  -.0023321   .0008587    -2.72   0.007    -.0040151    -.000649
    pol5_using |    .166394   .0135328    12.30   0.000     .1398703    .1929177
         _cons |  -2.040866   .0442397   -46.13   0.000    -2.127574   -1.954157
--------------------------------------------------------------------------------
(est2 stored)

. eststo:  probit atopally0 W_Regionalb  arep_MBallSW aiis_bl S I jointenemy_dum sqrtdist mpctdum poldif_using pol5_using, cl(di
> rdyadID)

Iteration 0:   log pseudolikelihood = -42790.566  
Iteration 1:   log pseudolikelihood = -37563.136  
Iteration 2:   log pseudolikelihood = -36208.322  
Iteration 3:   log pseudolikelihood = -36193.448  
Iteration 4:   log pseudolikelihood = -36193.427  
Iteration 5:   log pseudolikelihood = -36193.427  

Probit regression                               Number of obs     =  1,045,707
                                                Wald chi2(10)     =   10043.44
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -36193.427               Pseudo R2         =     0.1542

                           (Std. Err. adjusted for 27,874 clusters in dirdyadID)
--------------------------------------------------------------------------------
               |               Robust
     atopally0 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
   W_Regionalb |  -3.085453   .1114516   -27.68   0.000    -3.303894   -2.867012
  arep_MBallSW |   1.620181   .1986987     8.15   0.000     1.230738    2.009623
       aiis_bl |   .8344268    .632221     1.32   0.187    -.4047035    2.073557
             S |   .7680878   .0405633    18.94   0.000     .6885852    .8475904
             I |   .1797695   .0642304     2.80   0.005     .0538802    .3056588
jointenemy_dum |   .4850947   .0127601    38.02   0.000     .4600853     .510104
      sqrtdist |  -.0123781   .0002129   -58.14   0.000    -.0127954   -.0119608
       mpctdum |  -.2684401    .022172   -12.11   0.000    -.3118965   -.2249838
  poldif_using |  -.0012391   .0008639    -1.43   0.151    -.0029323    .0004541
    pol5_using |   .1965493   .0135187    14.54   0.000     .1700532    .2230454
         _cons |  -1.376387    .055335   -24.87   0.000    -1.484842   -1.267932
--------------------------------------------------------------------------------
(est3 stored)

. eststo:  probit atopally0              arep_MBallSW aiis_bl S I jointenemy_dum sqrtdist mpctdum poldif_using pol5_using if pol
> rel==1, cl(dirdyadID)

Iteration 0:   log pseudolikelihood = -14652.562  
Iteration 1:   log pseudolikelihood = -13550.867  
Iteration 2:   log pseudolikelihood = -13466.922  
Iteration 3:   log pseudolikelihood = -13466.756  
Iteration 4:   log pseudolikelihood = -13466.756  

Probit regression                               Number of obs     =    162,456
                                                Wald chi2(9)      =    1862.75
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -13466.756               Pseudo R2         =     0.0809

                            (Std. Err. adjusted for 3,452 clusters in dirdyadID)
--------------------------------------------------------------------------------
               |               Robust
     atopally0 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
  arep_MBallSW |    .020526   .3316741     0.06   0.951    -.6295433    .6705952
       aiis_bl |   .8557379   .5498932     1.56   0.120    -.2220329    1.933509
             S |   .5914873   .0720199     8.21   0.000     .4503309    .7326438
             I |  -.0810664   .0531448    -1.53   0.127    -.1852283    .0230956
jointenemy_dum |   .3865336    .019356    19.97   0.000     .3485966    .4244705
      sqrtdist |  -.0088284   .0003888   -22.71   0.000    -.0095904   -.0080664
       mpctdum |   .0696372    .026459     2.63   0.008     .0177785    .1214958
  poldif_using |   .0054232   .0015675     3.46   0.001     .0023511    .0084954
    pol5_using |   .2329564   .0216519    10.76   0.000     .1905194    .2753933
         _cons |  -2.546301   .0699986   -36.38   0.000    -2.683496   -2.409106
--------------------------------------------------------------------------------
(est4 stored)

. eststo:  probit atopally0 W_SystemRELa arep_MBallSW aiis_bl S I jointenemy_dum sqrtdist mpctdum poldif_using pol5_using if pol
> rel==1, cl(dirdyadID)

Iteration 0:   log pseudolikelihood = -14652.562  
Iteration 1:   log pseudolikelihood = -13420.103  
Iteration 2:   log pseudolikelihood = -13314.291  
Iteration 3:   log pseudolikelihood = -13313.994  
Iteration 4:   log pseudolikelihood = -13313.994  

Probit regression                               Number of obs     =    162,456
                                                Wald chi2(10)     =    2462.80
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -13313.994               Pseudo R2         =     0.0914

                            (Std. Err. adjusted for 3,452 clusters in dirdyadID)
--------------------------------------------------------------------------------
               |               Robust
     atopally0 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
  W_SystemRELa |  -1.856628   .1240856   -14.96   0.000    -2.099832   -1.613425
  arep_MBallSW |   .3117881   .3143966     0.99   0.321     -.304418    .9279942
       aiis_bl |   .9212819   .5809398     1.59   0.113    -.2173391    2.059903
             S |   .6228017   .0686153     9.08   0.000     .4883181    .7572853
             I |  -.0878779   .0526554    -1.67   0.095    -.1910805    .0153248
jointenemy_dum |   .3785707   .0198322    19.09   0.000     .3397003     .417441
      sqrtdist |  -.0096544   .0004006   -24.10   0.000    -.0104396   -.0088692
       mpctdum |   .1751205   .0271966     6.44   0.000     .1218162    .2284248
  poldif_using |   .0023605   .0016259     1.45   0.147    -.0008262    .0055472
    pol5_using |   .1456874     .02309     6.31   0.000     .1004319     .190943
         _cons |  -2.079589   .0771469   -26.96   0.000    -2.230794   -1.928384
--------------------------------------------------------------------------------
(est5 stored)

. 
. 
. esttab , b(a2) se(2) replace label star(* 0.10 ** 0.05 *** 0.01) scalars(N chi2  ll ) pr2(4) varwidth(25) modelwidth(9)  ///
> title(Table 1: Uncertainty and Alliance Formation)       ///
> nonumbers   ///
> mtitles("Model A1" "Model A2" "Model A3" "Model A4" "Model A5" "Model A6")  ///
> order(W_SystemRELa W_Regionalb  arep_MBallSW) ///
> nogap

Table 1: Uncertainty and Alliance Formation
------------------------------------------------------------------------------------------
                           Model A1     Model A2     Model A3     Model A4     Model A5   
------------------------------------------------------------------------------------------
atopally0                                                                                 
W_SystemRELa                               -2.05***                               -1.86***
                                          (0.09)                                 (0.12)   
W_Regionalb                                             -3.09***                          
                                                       (0.11)                             
Alliance Reputation Sco~i      1.44***      1.71***      1.62***     0.021         0.31   
                             (0.20)       (0.20)       (0.20)       (0.33)       (0.31)   
Alliance Interstate Int~       0.62         0.76         0.83         0.86         0.92   
                             (0.61)       (0.63)       (0.63)       (0.55)       (0.58)   
s score, unweighted, gl~p      0.62***      0.64***      0.77***      0.59***      0.62***
                             (0.04)       (0.04)       (0.04)       (0.07)       (0.07)   
IIS_bl                         0.20***      0.17***      0.18***    -0.081       -0.088*  
                             (0.06)       (0.06)       (0.06)       (0.05)       (0.05)   
jointenemy_dum                 0.55***      0.55***      0.49***      0.39***      0.38***
                             (0.01)       (0.01)       (0.01)       (0.02)       (0.02)   
distance                     -0.012***    -0.012***    -0.012***   -0.0088***   -0.0097***
                             (0.00)       (0.00)       (0.00)       (0.00)       (0.00)   
major power status             0.12***      0.21***     -0.27***     0.070***      0.18***
                             (0.01)       (0.02)       (0.02)       (0.03)       (0.03)   
poldif_using               -0.00091      -0.0023***   -0.0012       0.0054***    0.0024   
                             (0.00)       (0.00)       (0.00)       (0.00)       (0.00)   
pol5_using                     0.21***      0.17***      0.20***      0.23***      0.15***
                             (0.01)       (0.01)       (0.01)       (0.02)       (0.02)   
Constant                      -2.54***     -2.04***     -1.38***     -2.55***     -2.08***
                             (0.04)       (0.04)       (0.06)       (0.07)       (0.08)   
------------------------------------------------------------------------------------------
Observations                1045707      1045707      1045707       162456       162456   
Pseudo R-squared             0.1396       0.1473       0.1542       0.0809       0.0914   
chi2                        10226.4      10932.7      10043.4       1862.7       2462.8   
ll                         -36816.3     -36485.4     -36193.4     -13466.8     -13314.0   
------------------------------------------------------------------------------------------
Standard errors in parentheses
* p<0.10, ** p<0.05, *** p<0.01

. 
. 
. *********************************************
. ******SIMULATIONS**************************** -OK 
. *********************************************
. ***********Pr(Alliance Formation|Uncertainty)
. set seed 12345

. estsimp probit atopally0 W_SystemRELa arep_MBallSW aiis_bl S I jointenemy_dum sqrtdist mpctdum poldif_using pol5_using, cl(dir
> dyadID)

Iteration 0:   log pseudolikelihood = -42790.566
Iteration 1:   log pseudolikelihood = -37844.773
Iteration 2:   log pseudolikelihood = -36524.404
Iteration 3:   log pseudolikelihood = -36485.476
Iteration 4:   log pseudolikelihood = -36485.379
Iteration 5:   log pseudolikelihood = -36485.379

Probit regression                                 Number of obs   =    1045707
                                                  Wald chi2(10)   =   10932.72
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -36485.379                 Pseudo R2       =     0.1473

                          (Std. Err. adjusted for 27874 clusters in dirdyadID)
------------------------------------------------------------------------------
             |               Robust
   atopally0 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
W_SystemRELa |  -2.051094   .0863023   -23.77   0.000    -2.220244   -1.881945
arep_MBallSW |   1.713341   .1991716     8.60   0.000     1.322972    2.103711
     aiis_bl |   .7586221   .6296862     1.20   0.228      -.47554    1.992784
           S |   .6399346   .0405546    15.78   0.000     .5604489    .7194202
           I |    .168354   .0620262     2.71   0.007     .0467849    .2899231
jointenemy~m |   .5542143   .0132693    41.77   0.000     .5282071    .5802216
    sqrtdist |  -.0123165    .000207   -59.50   0.000    -.0127222   -.0119108
     mpctdum |   .2100753   .0154515    13.60   0.000      .179791    .2403597
poldif_using |  -.0023321   .0008587    -2.72   0.007    -.0040151    -.000649
  pol5_using |    .166394   .0135328    12.30   0.000     .1398703    .1929177
       _cons |  -2.040866   .0442397   -46.13   0.000    -2.127574   -1.954157
------------------------------------------------------------------------------

Simulating main parameters.  Please wait....
% of simulations completed: 9% 18% 27% 36% 45% 54% 63% 72% 81% 90% 100% 

Number of simulations  : 1000
Names of new variables : b1 b2 b3 b4 b5 b6 b7 b8 b9 b10 b11

. *Average Case Simulations
. setx aiis_bl mean S mean I mean sqrtdist mean jointenemy_dum median mpctdum median poldif_using median pol5_using median

. clarifyone W_SystemRELa 100
(1,584,682 missing values generated)


    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
xaxis_W_Sy~a |        100     .347149    .0928266   .1887663   .5055317
(268,828 missing values generated)
(1,406 real changes made, 1,406 to missing)

. simqi, fd(prval(1)) changex(W_SystemRELa min max)

First Difference: W_SystemRELa min max

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
         dPr(atopal~0 = 1) |  -.0027458     .0000926    -.0029276   -.0025544

. setx W_SystemRELa min

. simqi, prval(1)

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
            Pr(atopal~0=1) |   .0030902     .0000812     .0029286    .0032488

. setx W_SystemRELa max

. simqi, prval(1)

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
            Pr(atopal~0=1) |   .0003443     .0000308     .0002858    .0004092

. 
. *Most Alliance Prone Simulations
. setx arep_MBallSW max aiis_bl max S max I max sqrtdist min jointenemy_dum max mpctdum max poldif_using min pol5_using min

. simqi, fd(prval(1)) changex(W_SystemRELa min max)

First Difference: W_SystemRELa min max

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
         dPr(atopal~0 = 1) |   -.229191      .035892    -.2716583   -.1301497

. setx W_SystemRELa min

. simqi, prval(1)

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
            Pr(atopal~0=1) |   .7100051     .1394697     .3941611    .9350292

. setx W_SystemRELa max

. simqi, prval(1)

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
            Pr(atopal~0=1) |   .4808141     .1609212     .1777751    .8059935

. 
. 
. 
. 
. 
. *********************************************
. **********TABLE 3****************************  - OK
. *********************************************
. *In-Sample and Out-of-Sample Performance - Alliance Formation
. 
. cd  "C:\Users\Dell\Dropbox\OutcomeUncertainty\II Replication Folder"
C:\Users\Dell\Dropbox\OutcomeUncertainty\II Replication Folder

. 
. clear

. use "Alliance Formation Data"

. g perrornull=.
(1,584,782 missing values generated)

. g perrorSys=.
(1,584,782 missing values generated)

. g perrorReg=.
(1,584,782 missing values generated)

. g perrornullPR=.
(1,584,782 missing values generated)

. g perrorSysPR=.
(1,584,782 missing values generated)

. 
. qui{

. su perrornull perrorSys perrorReg perrornullPR perrorSysPR

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
  perrornull |  1,045,707    .0353055     .354725   .0000983   8.426505
   perrorSys |  1,045,707    .0349984    .3510167   .0000486   8.344071
   perrorReg |  1,045,707    .0347257    .3495708   .0000196   8.568253
perrornullPR |    162,456    .0831827    .4941414   .0003193   6.988935
 perrorSysPR |    162,456    .0822754     .487067   .0003001   6.733152

. 
. g cvlnull=-(atopally0*log(0.5)+(1-atopally0)*log(1-0.5))
(11,886 missing values generated)

. su cvlnull

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
     cvlnull |  1,572,896    .6931472           0   .6931472   .6931472

. scalar cvlnulla=r(mean)

. 
. foreach v in perrornull perrorSys perrorReg  perrornullPR perrorSysPR {
  2. qui su `v'
  3. qui scalar PRI`v'=(.6931472-r(mean))/.6931472
  4. di  PRI`v'
  5. }
.94906492
.94950797
.94990134
.8799927
.88130176

. 
. 
. 
. *********************************************
. *APPENDIX TABLE 1**************************** - OK
. *********************************************
. *Defense Pacts
. eststo clear

. eststo:  probit atopally0def arep_MBdefSW aiis_bl S I jointenemy_dum sqrtdist mpctdum poldif_using pol5_using, cl(dirdyadID)

Iteration 0:   log pseudolikelihood = -28899.048  
Iteration 1:   log pseudolikelihood = -25952.217  
Iteration 2:   log pseudolikelihood = -24265.749  
Iteration 3:   log pseudolikelihood = -24247.932  
Iteration 4:   log pseudolikelihood = -24247.919  
Iteration 5:   log pseudolikelihood = -24247.919  

Probit regression                               Number of obs     =  1,045,707
                                                Wald chi2(9)      =    9012.83
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -24247.919               Pseudo R2         =     0.1609

                           (Std. Err. adjusted for 27,874 clusters in dirdyadID)
--------------------------------------------------------------------------------
               |               Robust
  atopally0def |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
  arep_MBdefSW |   .5639911   .1896409     2.97   0.003     .1923018    .9356804
       aiis_bl |   1.004783   .6064829     1.66   0.098    -.1839013    2.193468
             S |   .7881342   .0597302    13.19   0.000     .6710652    .9052032
             I |    .445258   .0978906     4.55   0.000      .253396      .63712
jointenemy_dum |   .8035715   .0147696    54.41   0.000     .7746236    .8325195
      sqrtdist |   -.008131   .0002497   -32.56   0.000    -.0086204   -.0076416
       mpctdum |   .0445064   .0187182     2.38   0.017     .0078194    .0811934
  poldif_using |  -.0135864   .0011147   -12.19   0.000    -.0157712   -.0114016
    pol5_using |  -.0497459   .0188655    -2.64   0.008    -.0867216   -.0127701
         _cons |  -2.997361   .0555576   -53.95   0.000    -3.106252   -2.888471
--------------------------------------------------------------------------------
(est1 stored)

. eststo:  probit atopally0def W_SystemRELa arep_MBdefSW aiis_bl S I jointenemy_dum sqrtdist mpctdum poldif_using pol5_using, cl
> (dirdyadID)

Iteration 0:   log pseudolikelihood = -28899.048  
Iteration 1:   log pseudolikelihood = -25784.864  
Iteration 2:   log pseudolikelihood = -24060.869  
Iteration 3:   log pseudolikelihood = -24042.949  
Iteration 4:   log pseudolikelihood = -24042.935  
Iteration 5:   log pseudolikelihood = -24042.935  

Probit regression                               Number of obs     =  1,045,707
                                                Wald chi2(10)     =    8901.01
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -24042.935               Pseudo R2         =     0.1680

                           (Std. Err. adjusted for 27,874 clusters in dirdyadID)
--------------------------------------------------------------------------------
               |               Robust
  atopally0def |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
  W_SystemRELa |  -1.932441   .1042629   -18.53   0.000    -2.136793    -1.72809
  arep_MBdefSW |   .7250865   .1844926     3.93   0.000     .3634876    1.086685
       aiis_bl |   1.148745   .6195487     1.85   0.064    -.0655482    2.363038
             S |   .8152669   .0573165    14.22   0.000     .7029285    .9276052
             I |   .3992237   .0923684     4.32   0.000     .2181849    .5802625
jointenemy_dum |   .8155699   .0150688    54.12   0.000     .7860355    .8451042
      sqrtdist |  -.0083343   .0002629   -31.71   0.000    -.0088495   -.0078191
       mpctdum |   .1293207   .0201998     6.40   0.000     .0897298    .1689116
  poldif_using |  -.0144377    .001112   -12.98   0.000    -.0166173   -.0122581
    pol5_using |  -.0952785    .018996    -5.02   0.000      -.13251   -.0580471
         _cons |  -2.551718   .0602626   -42.34   0.000    -2.669831   -2.433606
--------------------------------------------------------------------------------
(est2 stored)

. 
. esttab, b(a2) se(2) replace label star(* 0.10 ** 0.05 *** 0.01) scalars(N chi2  ll ) pr2(4) varwidth(25) modelwidth(9)  ///
> title(Uncertainty and Alliance Formation)       ///
> nonumbers   ///
> mtitles("Model 1" "Model 2")  ///
> order(W_SystemRELa   arep_MBdefSW) ///
> nogap

Uncertainty and Alliance Formation
---------------------------------------------------
                            Model 1      Model 2   
---------------------------------------------------
atopally0def                                       
W_SystemRELa                               -1.93***
                                          (0.10)   
Alliance Reputation Sco~i      0.56***      0.73***
                             (0.19)       (0.18)   
Alliance Interstate Int~       1.00*        1.15*  
                             (0.61)       (0.62)   
s score, unweighted, gl~p      0.79***      0.82***
                             (0.06)       (0.06)   
IIS_bl                         0.45***      0.40***
                             (0.10)       (0.09)   
jointenemy_dum                 0.80***      0.82***
                             (0.01)       (0.02)   
distance                    -0.0081***   -0.0083***
                             (0.00)       (0.00)   
major power status            0.045**       0.13***
                             (0.02)       (0.02)   
poldif_using                 -0.014***    -0.014***
                             (0.00)       (0.00)   
pol5_using                   -0.050***    -0.095***
                             (0.02)       (0.02)   
Constant                      -3.00***     -2.55***
                             (0.06)       (0.06)   
---------------------------------------------------
Observations                1045707      1045707   
Pseudo R-squared             0.1609       0.1680   
chi2                         9012.8       8901.0   
ll                         -24247.9     -24042.9   
---------------------------------------------------
Standard errors in parentheses
* p<0.10, ** p<0.05, *** p<0.01

. 
. 
. *********************************************
. *APPENDIX TABLE 2**************************** - OK
. *********************************************
. *Multinomial
. g allytype=2 if atopally0def==1
(1,578,081 missing values generated)

. replace allytype=1 if atopally0def==0 & atopally0==1
(3,942 real changes made)

. replace allytype=0  if atopally0==0
(1,562,253 real changes made)

. 
. /*
> 
>    allytype  |      Freq.     Percent        Cum.
> -------------+-----------------------------------
>     Defense 2|      4,480        0.43        0.43
> No Alliance 0|  1,038,553       99.32       99.74
> Non-Defense 1|      2,674        0.26      100.00
> -------------+-----------------------------------
>       Total  |  1,045,707      100.00
> 
> */
. 
. eststo clear

. eststo: mlogit allytype arep_MBallSW aiis_bl S I jointenemy_dum sqrtdist mpctdum poldif_using pol5_using, cl(dirdyadID) base(0
> )

Iteration 0:   log pseudolikelihood = -47518.897  
Iteration 1:   log pseudolikelihood = -42285.138  
Iteration 2:   log pseudolikelihood = -40778.405  
Iteration 3:   log pseudolikelihood = -40534.565  
Iteration 4:   log pseudolikelihood = -40490.774  
Iteration 5:   log pseudolikelihood = -40481.046  
Iteration 6:   log pseudolikelihood = -40480.776  
Iteration 7:   log pseudolikelihood = -40480.767  
Iteration 8:   log pseudolikelihood = -40480.767  

Multinomial logistic regression                 Number of obs     =  1,045,707
                                                Wald chi2(18)     =   13046.82
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -40480.767               Pseudo R2         =     0.1481

                           (Std. Err. adjusted for 27,874 clusters in dirdyadID)
--------------------------------------------------------------------------------
               |               Robust
      allytype |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
0              |  (base outcome)
---------------+----------------------------------------------------------------
1              |
  arep_MBallSW |   2.185633   .6841785     3.19   0.001     .8446676    3.526598
       aiis_bl |   -2.88993   1.571895    -1.84   0.066    -5.970788    .1909278
             S |    1.11154   .1303725     8.53   0.000     .8560149    1.367066
             I |  -.4622099   .1951949    -2.37   0.018    -.8447849   -.0796349
jointenemy_dum |  -.2069761   .0569805    -3.63   0.000    -.3186559   -.0952963
      sqrtdist |  -.0437821   .0008799   -49.76   0.000    -.0455066   -.0420575
       mpctdum |   .6149932    .063326     9.71   0.000     .4908767    .7391098
  poldif_using |   .0580583   .0037334    15.55   0.000      .050741    .0653756
    pol5_using |    1.53423   .0594243    25.82   0.000      1.41776    1.650699
         _cons |   -5.56934   .1206971   -46.14   0.000    -5.805902   -5.332778
---------------+----------------------------------------------------------------
2              |
  arep_MBallSW |   2.777846   .4554523     6.10   0.000     1.885176    3.670517
       aiis_bl |   1.763296   1.018812     1.73   0.083    -.2335385    3.760131
             S |   2.677833   .2090201    12.81   0.000     2.268161    3.087505
             I |   1.084614   .2486364     4.36   0.000     .5972954    1.571932
jointenemy_dum |   2.081765   .0461913    45.07   0.000     1.991232    2.172298
      sqrtdist |  -.0203491   .0007036   -28.92   0.000    -.0217281   -.0189702
       mpctdum |   .1621579   .0487858     3.32   0.001     .0665394    .2577764
  poldif_using |  -.0338952   .0031711   -10.69   0.000    -.0401104   -.0276799
    pol5_using |  -.1553793   .0488543    -3.18   0.001     -.251132   -.0596266
         _cons |  -7.033362    .187218   -37.57   0.000    -7.400303   -6.666421
--------------------------------------------------------------------------------
(est1 stored)

. eststo: mlogit allytype W_SystemRELa arep_MBallSW aiis_bl S I jointenemy_dum sqrtdist mpctdum poldif_using pol5_using, cl(dird
> yadID) base(0)

Iteration 0:   log pseudolikelihood = -47518.897  
Iteration 1:   log pseudolikelihood = -42112.808  
Iteration 2:   log pseudolikelihood = -40516.804  
Iteration 3:   log pseudolikelihood = -40312.674  
Iteration 4:   log pseudolikelihood = -40296.828  
Iteration 5:   log pseudolikelihood = -40251.421  
Iteration 6:   log pseudolikelihood = -40206.565  
Iteration 7:   log pseudolikelihood = -40193.222  
Iteration 8:   log pseudolikelihood = -40192.509  
Iteration 9:   log pseudolikelihood = -40191.282  
Iteration 10:  log pseudolikelihood = -40191.272  
Iteration 11:  log pseudolikelihood = -40191.272  

Multinomial logistic regression                 Number of obs     =  1,045,707
                                                Wald chi2(20)     =   14012.48
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -40191.272               Pseudo R2         =     0.1542

                           (Std. Err. adjusted for 27,874 clusters in dirdyadID)
--------------------------------------------------------------------------------
               |               Robust
      allytype |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
0              |  (base outcome)
---------------+----------------------------------------------------------------
1              |
  W_SystemRELa |   -4.99214   .3988761   -12.52   0.000    -5.773923   -4.210357
  arep_MBallSW |   3.063664    .667541     4.59   0.000     1.755308     4.37202
       aiis_bl |  -2.682444   1.633025    -1.64   0.100    -5.883113    .5182254
             S |   1.077037   .1297947     8.30   0.000     .8226439     1.33143
             I |  -.4854135   .1894812    -2.56   0.010    -.8567898   -.1140373
jointenemy_dum |  -.2391462   .0583503    -4.10   0.000    -.3535106   -.1247817
      sqrtdist |  -.0454471    .000856   -53.10   0.000    -.0471248   -.0437695
       mpctdum |   .8232184    .062515    13.17   0.000     .7006913    .9457454
  poldif_using |   .0521154   .0036198    14.40   0.000     .0450207      .05921
    pol5_using |   1.395482   .0594667    23.47   0.000     1.278929    1.512035
         _cons |  -4.229205   .1585584   -26.67   0.000    -4.539974   -3.918436
---------------+----------------------------------------------------------------
2              |
  W_SystemRELa |  -4.611297   .2754771   -16.74   0.000    -5.151223   -4.071372
  arep_MBallSW |   3.297482    .453967     7.26   0.000     2.407723    4.187241
       aiis_bl |   2.094646   1.056254     1.98   0.047      .024426    4.164866
             S |   2.658701   .1954781    13.60   0.000     2.275571    3.041831
             I |   .9789499   .2291221     4.27   0.000     .5298789    1.428021
jointenemy_dum |   2.091298   .0471048    44.40   0.000     1.998975    2.183622
      sqrtdist |   -.020771   .0007475   -27.79   0.000    -.0222361   -.0193059
       mpctdum |   .3534775    .052938     6.68   0.000      .249721     .457234
  poldif_using |  -.0352385   .0031443   -11.21   0.000    -.0414013   -.0290757
    pol5_using |  -.2549101   .0492323    -5.18   0.000    -.3514036   -.1584166
         _cons |  -5.895815   .1939104   -30.40   0.000    -6.275873   -5.515758
--------------------------------------------------------------------------------
(est2 stored)

. eststo: mlogit allytype W_Regionalb  arep_MBallSW aiis_bl S I jointenemy_dum sqrtdist mpctdum poldif_using pol5_using, cl(dird
> yadID) base(0)

Iteration 0:   log pseudolikelihood = -47518.897  
Iteration 1:   log pseudolikelihood = -41790.514  
Iteration 2:   log pseudolikelihood = -39973.134  
Iteration 3:   log pseudolikelihood = -39741.074  
Iteration 4:   log pseudolikelihood = -39625.756  
Iteration 5:   log pseudolikelihood = -39578.484  
Iteration 6:   log pseudolikelihood = -39574.741  
Iteration 7:   log pseudolikelihood = -39572.378  
Iteration 8:   log pseudolikelihood = -39572.354  
Iteration 9:   log pseudolikelihood = -39572.354  

Multinomial logistic regression                 Number of obs     =  1,045,707
                                                Wald chi2(20)     =   13054.96
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -39572.354               Pseudo R2         =     0.1672

                           (Std. Err. adjusted for 27,874 clusters in dirdyadID)
--------------------------------------------------------------------------------
               |               Robust
      allytype |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
0              |  (base outcome)
---------------+----------------------------------------------------------------
1              |
   W_Regionalb |  -.8848393   .3300335    -2.68   0.007    -1.531693   -.2379856
  arep_MBallSW |   2.227795   .6793283     3.28   0.001     .8963365    3.559254
       aiis_bl |  -2.879248   1.582876    -1.82   0.069    -5.981628    .2231306
             S |   1.158296   .1317862     8.79   0.000           .9    1.416592
             I |  -.4606283   .1950882    -2.36   0.018    -.8429943   -.0782624
jointenemy_dum |  -.2234648   .0580636    -3.85   0.000    -.3372673   -.1096623
      sqrtdist |  -.0439414   .0008699   -50.51   0.000    -.0456464   -.0422365
       mpctdum |   .5029826   .0751851     6.69   0.000     .3556225    .6503427
  poldif_using |    .057474   .0037174    15.46   0.000     .0501881      .06476
    pol5_using |   1.519664   .0597362    25.44   0.000     1.402583    1.636745
         _cons |  -5.225578   .1706373   -30.62   0.000    -5.560021   -4.891135
---------------+----------------------------------------------------------------
2              |
   W_Regionalb |  -12.43054   .4445359   -27.96   0.000    -13.30181   -11.55926
  arep_MBallSW |   3.201603   .4819329     6.64   0.000     2.257032    4.146174
       aiis_bl |   2.676709   1.144634     2.34   0.019     .4332682    4.920149
             S |   3.032015   .1888759    16.05   0.000     2.661826    3.402205
             I |    1.03824   .2467689     4.21   0.000     .5545822    1.521898
jointenemy_dum |    1.79951   .0448244    40.15   0.000     1.711655    1.887364
      sqrtdist |  -.0220024   .0007802   -28.20   0.000    -.0235315   -.0204733
       mpctdum |  -1.430435   .0839984   -17.03   0.000    -1.595069   -1.265801
  poldif_using |  -.0344817   .0031975   -10.78   0.000    -.0407488   -.0282147
    pol5_using |   -.237821   .0497801    -4.78   0.000    -.3353882   -.1402539
         _cons |  -2.230419   .2384048    -9.36   0.000    -2.697684   -1.763154
--------------------------------------------------------------------------------
(est3 stored)

. 
. esttab, b(a2) se(2) replace label star(* 0.10 ** 0.05 *** 0.01) scalars(N chi2  ll ) pr2(4) varwidth(25) modelwidth(9)  ///
> title(Multinomial Logit Analyses of Alliance Formation)       ///
> nonumbers   ///
> order(W_SystemRELa W_Regionalb) ///
> addnote("") ///
> nogap

Multinomial Logit Analyses of Alliance Formation
----------------------------------------------------------------
                           allytype     allytype     allytype   
----------------------------------------------------------------
0                                                               
W_SystemRELa                                   0                
                                             (.)                
W_Regionalb                                                 0   
                                                          (.)   
Alliance Reputation Sco~i         0            0            0   
                                (.)          (.)          (.)   
Alliance Interstate Int~          0            0            0   
                                (.)          (.)          (.)   
s score, unweighted, gl~p         0            0            0   
                                (.)          (.)          (.)   
IIS_bl                            0            0            0   
                                (.)          (.)          (.)   
jointenemy_dum                    0            0            0   
                                (.)          (.)          (.)   
distance                          0            0            0   
                                (.)          (.)          (.)   
major power status                0            0            0   
                                (.)          (.)          (.)   
poldif_using                      0            0            0   
                                (.)          (.)          (.)   
pol5_using                        0            0            0   
                                (.)          (.)          (.)   
Constant                          0            0            0   
                                (.)          (.)          (.)   
----------------------------------------------------------------
1                                                               
W_SystemRELa                               -4.99***             
                                          (0.40)                
W_Regionalb                                             -0.88***
                                                       (0.33)   
Alliance Reputation Sco~i      2.19***      3.06***      2.23***
                             (0.68)       (0.67)       (0.68)   
Alliance Interstate Int~      -2.89*       -2.68        -2.88*  
                             (1.57)       (1.63)       (1.58)   
s score, unweighted, gl~p      1.11***      1.08***      1.16***
                             (0.13)       (0.13)       (0.13)   
IIS_bl                        -0.46**      -0.49**      -0.46** 
                             (0.20)       (0.19)       (0.20)   
jointenemy_dum                -0.21***     -0.24***     -0.22***
                             (0.06)       (0.06)       (0.06)   
distance                     -0.044***    -0.045***    -0.044***
                             (0.00)       (0.00)       (0.00)   
major power status             0.61***      0.82***      0.50***
                             (0.06)       (0.06)       (0.08)   
poldif_using                  0.058***     0.052***     0.057***
                             (0.00)       (0.00)       (0.00)   
pol5_using                     1.53***      1.40***      1.52***
                             (0.06)       (0.06)       (0.06)   
Constant                      -5.57***     -4.23***     -5.23***
                             (0.12)       (0.16)       (0.17)   
----------------------------------------------------------------
2                                                               
W_SystemRELa                               -4.61***             
                                          (0.28)                
W_Regionalb                                             -12.4***
                                                       (0.44)   
Alliance Reputation Sco~i      2.78***      3.30***      3.20***
                             (0.46)       (0.45)       (0.48)   
Alliance Interstate Int~       1.76*        2.09**       2.68** 
                             (1.02)       (1.06)       (1.14)   
s score, unweighted, gl~p      2.68***      2.66***      3.03***
                             (0.21)       (0.20)       (0.19)   
IIS_bl                         1.08***      0.98***      1.04***
                             (0.25)       (0.23)       (0.25)   
jointenemy_dum                 2.08***      2.09***      1.80***
                             (0.05)       (0.05)       (0.04)   
distance                     -0.020***    -0.021***    -0.022***
                             (0.00)       (0.00)       (0.00)   
major power status             0.16***      0.35***     -1.43***
                             (0.05)       (0.05)       (0.08)   
poldif_using                 -0.034***    -0.035***    -0.034***
                             (0.00)       (0.00)       (0.00)   
pol5_using                    -0.16***     -0.25***     -0.24***
                             (0.05)       (0.05)       (0.05)   
Constant                      -7.03***     -5.90***     -2.23***
                             (0.19)       (0.19)       (0.24)   
----------------------------------------------------------------
Observations                1045707      1045707      1045707   
Pseudo R-squared             0.1481       0.1542       0.1672   
chi2                        13046.8      14012.5      13055.0   
ll                         -40480.8     -40191.3     -39572.4   
----------------------------------------------------------------
Standard errors in parentheses

* p<0.10, ** p<0.05, *** p<0.01

. 
. 
. 
. *********************************************
. *APPENDIX TABLE 6**************************** - OK
. *********************************************
. *Kenkel-Carroll Measure
. eststo clear

. foreach v in SystemE W_SystemE W_System_d1E W_System_d2E W_System_d1cE W_System_d2cE {
  2. eststo:  probit atopally0    `v'          arep_MBallSW aiis_bl S I jointenemy_dum sqrtdist mpctdum poldif_using pol5_using,
>  cl(dirdyadID)
  3. }

Iteration 0:   log pseudolikelihood = -42790.566  
Iteration 1:   log pseudolikelihood = -37863.597  
Iteration 2:   log pseudolikelihood = -36521.888  
Iteration 3:   log pseudolikelihood = -36513.075  
Iteration 4:   log pseudolikelihood = -36513.068  
Iteration 5:   log pseudolikelihood = -36513.068  

Probit regression                               Number of obs     =  1,045,707
                                                Wald chi2(10)     =   10894.03
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -36513.068               Pseudo R2         =     0.1467

                           (Std. Err. adjusted for 27,874 clusters in dirdyadID)
--------------------------------------------------------------------------------
               |               Robust
     atopally0 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
       SystemE |   -.666474    .027829   -23.95   0.000    -.7210178   -.6119302
  arep_MBallSW |   1.705406   .1980881     8.61   0.000      1.31716    2.093651
       aiis_bl |   .6529138   .6264811     1.04   0.297    -.5749666    1.880794
             S |   .6058354   .0414089    14.63   0.000     .5246754    .6869954
             I |    .156094   .0609079     2.56   0.010     .0367168    .2754712
jointenemy_dum |   .5672021   .0132403    42.84   0.000     .5412516    .5931526
      sqrtdist |  -.0123974    .000209   -59.33   0.000     -.012807   -.0119879
       mpctdum |    .203375   .0154561    13.16   0.000     .1730816    .2336685
  poldif_using |  -.0028022   .0008624    -3.25   0.001    -.0044926   -.0011119
    pol5_using |   .1515189   .0135504    11.18   0.000     .1249606    .1780773
         _cons |  -2.211145   .0413048   -53.53   0.000    -2.292101   -2.130189
--------------------------------------------------------------------------------
(est1 stored)

Iteration 0:   log pseudolikelihood = -42790.566  
Iteration 1:   log pseudolikelihood = -37966.738  
Iteration 2:   log pseudolikelihood = -36645.986  
Iteration 3:   log pseudolikelihood = -36638.396  
Iteration 4:   log pseudolikelihood = -36638.391  
Iteration 5:   log pseudolikelihood = -36638.391  

Probit regression                               Number of obs     =  1,045,707
                                                Wald chi2(10)     =   10870.88
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -36638.391               Pseudo R2         =     0.1438

                           (Std. Err. adjusted for 27,874 clusters in dirdyadID)
--------------------------------------------------------------------------------
               |               Robust
     atopally0 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
     W_SystemE |  -.5292832   .0282457   -18.74   0.000    -.5846437   -.4739226
  arep_MBallSW |   1.678722   .1977818     8.49   0.000     1.291076    2.066367
       aiis_bl |   .6613444   .6202594     1.07   0.286    -.5543416     1.87703
             S |   .6088455   .0414424    14.69   0.000     .5276199    .6900712
             I |   .1650839   .0615912     2.68   0.007     .0443674    .2858005
jointenemy_dum |   .5653405    .013042    43.35   0.000     .5397786    .5909024
      sqrtdist |  -.0122195    .000207   -59.04   0.000    -.0126251   -.0118138
       mpctdum |   .1811127   .0154611    11.71   0.000     .1508095    .2114159
  poldif_using |  -.0023433   .0008582    -2.73   0.006    -.0040254   -.0006612
    pol5_using |   .1665279   .0134097    12.42   0.000     .1402453    .1928104
         _cons |  -2.254801   .0410902   -54.87   0.000    -2.335336   -2.174266
--------------------------------------------------------------------------------
(est2 stored)

Iteration 0:   log pseudolikelihood = -42790.566  
Iteration 1:   log pseudolikelihood = -37879.167  
Iteration 2:   log pseudolikelihood = -36540.766  
Iteration 3:   log pseudolikelihood = -36532.144  
Iteration 4:   log pseudolikelihood = -36532.138  
Iteration 5:   log pseudolikelihood = -36532.138  

Probit regression                               Number of obs     =  1,045,707
                                                Wald chi2(10)     =   10914.11
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -36532.138               Pseudo R2         =     0.1463

                           (Std. Err. adjusted for 27,874 clusters in dirdyadID)
--------------------------------------------------------------------------------
               |               Robust
     atopally0 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
  W_System_d1E |  -.6400933   .0273457   -23.41   0.000    -.6936898   -.5864967
  arep_MBallSW |     1.7028   .1982101     8.59   0.000     1.314315    2.091285
       aiis_bl |   .6548952   .6255052     1.05   0.295    -.5710725    1.880863
             S |   .6050633   .0414323    14.60   0.000     .5238575     .686269
             I |   .1566585    .060932     2.57   0.010      .037234    .2760831
jointenemy_dum |   .5677865   .0132241    42.94   0.000     .5418677    .5937053
      sqrtdist |  -.0123713   .0002087   -59.28   0.000    -.0127803   -.0119622
       mpctdum |   .2001422    .015425    12.98   0.000     .1699098    .2303746
  poldif_using |   -.002758   .0008623    -3.20   0.001    -.0044481   -.0010678
    pol5_using |   .1522052   .0135458    11.24   0.000     .1256559    .1787545
         _cons |  -2.223601   .0412479   -53.91   0.000    -2.304446   -2.142757
--------------------------------------------------------------------------------
(est3 stored)

Iteration 0:   log pseudolikelihood = -42790.566  
Iteration 1:   log pseudolikelihood = -37880.007  
Iteration 2:   log pseudolikelihood = -36541.773  
Iteration 3:   log pseudolikelihood = -36533.161  
Iteration 4:   log pseudolikelihood = -36533.156  
Iteration 5:   log pseudolikelihood = -36533.156  

Probit regression                               Number of obs     =  1,045,707
                                                Wald chi2(10)     =   10914.31
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -36533.156               Pseudo R2         =     0.1462

                           (Std. Err. adjusted for 27,874 clusters in dirdyadID)
--------------------------------------------------------------------------------
               |               Robust
     atopally0 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
  W_System_d2E |  -.6386933   .0273246   -23.37   0.000    -.6922486   -.5851379
  arep_MBallSW |   1.702488   .1982071     8.59   0.000     1.314009    2.090967
       aiis_bl |   .6548226   .6254631     1.05   0.295    -.5710626    1.880708
             S |   .6050677   .0414329    14.60   0.000     .5238607    .6862748
             I |   .1567213   .0609359     2.57   0.010      .037289    .2761535
jointenemy_dum |   .5677798   .0132227    42.94   0.000     .5418638    .5936959
      sqrtdist |    -.01237   .0002087   -59.28   0.000     -.012779    -.011961
       mpctdum |   .1999784   .0154245    12.96   0.000     .1697469    .2302099
  poldif_using |  -.0027548   .0008623    -3.19   0.001    -.0044449   -.0010647
    pol5_using |   .1523078   .0135448    11.24   0.000     .1257604    .1788551
         _cons |   -2.22429   .0412413   -53.93   0.000    -2.305121   -2.143458
--------------------------------------------------------------------------------
(est4 stored)

Iteration 0:   log pseudolikelihood = -42790.566  
Iteration 1:   log pseudolikelihood = -37992.439  
Iteration 2:   log pseudolikelihood = -36676.281  
Iteration 3:   log pseudolikelihood = -36668.857  
Iteration 4:   log pseudolikelihood = -36668.852  
Iteration 5:   log pseudolikelihood = -36668.852  

Probit regression                               Number of obs     =  1,045,707
                                                Wald chi2(10)     =   10854.44
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -36668.852               Pseudo R2         =     0.1431

                           (Std. Err. adjusted for 27,874 clusters in dirdyadID)
--------------------------------------------------------------------------------
               |               Robust
     atopally0 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
 W_System_d1cE |  -.4833638   .0282758   -17.09   0.000    -.5387833   -.4279443
  arep_MBallSW |   1.667822    .197938     8.43   0.000     1.279871    2.055774
       aiis_bl |   .6600008   .6184007     1.07   0.286    -.5520424    1.872044
             S |   .6080561    .041499    14.65   0.000     .5267195    .6893927
             I |   .1672733   .0617269     2.71   0.007     .0462908    .2882559
jointenemy_dum |   .5655652   .0129925    43.53   0.000     .5401003    .5910301
      sqrtdist |  -.0121753   .0002066   -58.92   0.000    -.0125803   -.0117703
       mpctdum |   .1748762   .0154289    11.33   0.000     .1446361    .2051163
  poldif_using |  -.0022276   .0008576    -2.60   0.009    -.0039084   -.0005468
    pol5_using |   .1700726   .0133965    12.70   0.000     .1438158    .1963293
         _cons |   -2.27539   .0411475   -55.30   0.000    -2.356038   -2.194743
--------------------------------------------------------------------------------
(est5 stored)

Iteration 0:   log pseudolikelihood = -42790.566  
Iteration 1:   log pseudolikelihood = -37994.305  
Iteration 2:   log pseudolikelihood = -36678.483  
Iteration 3:   log pseudolikelihood =  -36671.07  
Iteration 4:   log pseudolikelihood = -36671.066  
Iteration 5:   log pseudolikelihood = -36671.066  

Probit regression                               Number of obs     =  1,045,707
                                                Wald chi2(10)     =   10854.95
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -36671.066               Pseudo R2         =     0.1430

                           (Std. Err. adjusted for 27,874 clusters in dirdyadID)
--------------------------------------------------------------------------------
               |               Robust
     atopally0 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
 W_System_d2cE |  -.4771692   .0281236   -16.97   0.000    -.5322905   -.4220479
  arep_MBallSW |   1.666984   .1979425     8.42   0.000     1.279024    2.054944
       aiis_bl |   .6601426   .6183139     1.07   0.286    -.5517304    1.872016
             S |    .608205   .0415006    14.66   0.000     .5268654    .6895447
             I |   .1674561   .0617438     2.71   0.007     .0464404    .2884718
jointenemy_dum |   .5655096   .0129882    43.54   0.000     .5400532     .590966
      sqrtdist |  -.0121718   .0002066   -58.91   0.000    -.0125767   -.0117668
       mpctdum |   .1744215    .015428    11.31   0.000     .1441831    .2046598
  poldif_using |  -.0022161   .0008575    -2.58   0.010    -.0038967   -.0005354
    pol5_using |   .1704473   .0133945    12.73   0.000     .1441945    .1967001
         _cons |  -2.279378   .0411035   -55.45   0.000    -2.359939   -2.198817
--------------------------------------------------------------------------------
(est6 stored)

. esttab , b(a2) se(2) replace  star(* 0.10 ** 0.05 *** 0.01) scalars(N chi2  ll ) pr2(4) varwidth(25) modelwidth(9)  ///
> title(Table 1: Uncertainty and Alliance Formation)       ///
> nonumbers   ///
> mtitles("Model A1" "Model A2" "Model A3" "Model A4" "Model A5" "Model A6")  ///
> order(SystemE W_SystemE W_System_d1E W_System_d2E W_System_d1cE W_System_d2cE) ///
> nogap

Table 1: Uncertainty and Alliance Formation
-------------------------------------------------------------------------------------------------------
                           Model A1     Model A2     Model A3     Model A4     Model A5     Model A6   
-------------------------------------------------------------------------------------------------------
atopally0                                                                                              
SystemE                       -0.67***                                                                 
                             (0.03)                                                                    
W_SystemE                                  -0.53***                                                    
                                          (0.03)                                                       
W_System_d1E                                            -0.64***                                       
                                                       (0.03)                                          
W_System_d2E                                                         -0.64***                          
                                                                    (0.03)                             
W_System_d1cE                                                                     -0.48***             
                                                                                 (0.03)                
W_System_d2cE                                                                                  -0.48***
                                                                                              (0.03)   
arep_MBallSW                   1.71***      1.68***      1.70***      1.70***      1.67***      1.67***
                             (0.20)       (0.20)       (0.20)       (0.20)       (0.20)       (0.20)   
aiis_bl                        0.65         0.66         0.65         0.65         0.66         0.66   
                             (0.63)       (0.62)       (0.63)       (0.63)       (0.62)       (0.62)   
S                              0.61***      0.61***      0.61***      0.61***      0.61***      0.61***
                             (0.04)       (0.04)       (0.04)       (0.04)       (0.04)       (0.04)   
I                              0.16**       0.17***      0.16**       0.16**       0.17***      0.17***
                             (0.06)       (0.06)       (0.06)       (0.06)       (0.06)       (0.06)   
jointenemy_dum                 0.57***      0.57***      0.57***      0.57***      0.57***      0.57***
                             (0.01)       (0.01)       (0.01)       (0.01)       (0.01)       (0.01)   
sqrtdist                     -0.012***    -0.012***    -0.012***    -0.012***    -0.012***    -0.012***
                             (0.00)       (0.00)       (0.00)       (0.00)       (0.00)       (0.00)   
mpctdum                        0.20***      0.18***      0.20***      0.20***      0.17***      0.17***
                             (0.02)       (0.02)       (0.02)       (0.02)       (0.02)       (0.02)   
poldif_using                -0.0028***   -0.0023***   -0.0028***   -0.0028***   -0.0022***   -0.0022***
                             (0.00)       (0.00)       (0.00)       (0.00)       (0.00)       (0.00)   
pol5_using                     0.15***      0.17***      0.15***      0.15***      0.17***      0.17***
                             (0.01)       (0.01)       (0.01)       (0.01)       (0.01)       (0.01)   
_cons                         -2.21***     -2.25***     -2.22***     -2.22***     -2.28***     -2.28***
                             (0.04)       (0.04)       (0.04)       (0.04)       (0.04)       (0.04)   
-------------------------------------------------------------------------------------------------------
N                           1045707      1045707      1045707      1045707      1045707      1045707   
pseudo R-sq                  0.1467       0.1438       0.1463       0.1462       0.1431       0.1430   
chi2                        10894.0      10870.9      10914.1      10914.3      10854.4      10854.9   
ll                         -36513.1     -36638.4     -36532.1     -36533.2     -36668.9     -36671.1   
-------------------------------------------------------------------------------------------------------
Standard errors in parentheses
* p<0.10, ** p<0.05, *** p<0.01

. 
. 
. *********************************************
. *APPENDIX TABLE 9**************************** - OK
. *********************************************
. *Hierarchical Models
. eststo clear

. eststo:  xtmelogit atopally0              arep_MBallSW aiis_bl S I jointenemy_dum sqrtdist mpctdum poldif_using pol5_using || 
> year:               , intpoints(10)

Refining starting values: 

Iteration 0:   log likelihood = -30172.073  (not concave)
Iteration 1:   log likelihood = -30049.101  
Iteration 2:   log likelihood = -29757.831  

Performing gradient-based optimization: 

Iteration 0:   log likelihood = -29757.831  
Iteration 1:   log likelihood = -29656.645  
Iteration 2:   log likelihood = -29566.449  
Iteration 3:   log likelihood =  -29565.38  
Iteration 4:   log likelihood = -29558.313  
Iteration 5:   log likelihood = -29558.309  
Iteration 6:   log likelihood = -29558.309  

Mixed-effects logistic regression               Number of obs     =  1,045,707
Group variable: year                            Number of groups  =        184

                                                Obs per group:
                                                              min =        304
                                                              avg =    5,683.2
                                                              max =     25,078

Integration points =  10                        Wald chi2(9)      =   10036.25
Log likelihood = -29558.309                     Prob > chi2       =     0.0000

--------------------------------------------------------------------------------
     atopally0 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
  arep_MBallSW |  -7.495532   .8257358    -9.08   0.000    -9.113945    -5.87712
       aiis_bl |  -3.600651   1.452977    -2.48   0.013    -6.448434   -.7528674
             S |    2.15607   .0889496    24.24   0.000     1.981732    2.330408
             I |    .499199   .1396331     3.58   0.000     .2255232    .7728748
jointenemy_dum |   .6288928   .0315904    19.91   0.000     .5669768    .6908087
      sqrtdist |  -.0355967   .0005544   -64.20   0.000    -.0366833     -.03451
       mpctdum |   .8121076   .0338747    23.97   0.000     .7457143    .8785008
  poldif_using |  -.0139145   .0023803    -5.85   0.000    -.0185799   -.0092492
    pol5_using |   .4659869   .0361111    12.90   0.000     .3952104    .5367633
         _cons |  -7.008844   .1874819   -37.38   0.000    -7.376302   -6.641386
--------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
year: Identity               |
                   sd(_cons) |   1.941813   .1435742      1.679852    2.244625
------------------------------------------------------------------------------
LR test vs. logistic model: chibar2(01) = 14952.96    Prob >= chibar2 = 0.0000
(est1 stored)

. eststo:  xtmelogit atopally0 W_SystemRELa arep_MBallSW aiis_bl S I jointenemy_dum sqrtdist mpctdum poldif_using pol5_using || 
> year:                       , intpoints(10)

Refining starting values: 

Iteration 0:   log likelihood = -30075.589  (not concave)
Iteration 1:   log likelihood =  -29905.68  
Iteration 2:   log likelihood =  -29658.98  

Performing gradient-based optimization: 

Iteration 0:   log likelihood =  -29658.98  
Iteration 1:   log likelihood = -29563.572  
Iteration 2:   log likelihood = -29543.409  
Iteration 3:   log likelihood = -29541.497  
Iteration 4:   log likelihood = -29541.468  
Iteration 5:   log likelihood = -29541.468  

Mixed-effects logistic regression               Number of obs     =  1,045,707
Group variable: year                            Number of groups  =        184

                                                Obs per group:
                                                              min =        304
                                                              avg =    5,683.2
                                                              max =     25,078

Integration points =  10                        Wald chi2(10)     =   10070.66
Log likelihood = -29541.468                     Prob > chi2       =     0.0000

--------------------------------------------------------------------------------
     atopally0 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
  W_SystemRELa |  -9.092933   1.586306    -5.73   0.000    -12.20204    -5.98383
  arep_MBallSW |  -7.482622   .8237074    -9.08   0.000    -9.097059   -5.868186
       aiis_bl |  -3.612597   1.453276    -2.49   0.013    -6.460965   -.7642292
             S |   2.153161   .0889171    24.22   0.000     1.978887    2.327435
             I |   .4992895   .1395729     3.58   0.000     .2257315    .7728474
jointenemy_dum |   .6272039   .0315916    19.85   0.000     .5652855    .6891222
      sqrtdist |  -.0356366   .0005546   -64.25   0.000    -.0367237   -.0345495
       mpctdum |    .818309   .0338691    24.16   0.000     .7519268    .8846913
  poldif_using |  -.0140322   .0023792    -5.90   0.000    -.0186954    -.009369
    pol5_using |   .4618578   .0361025    12.79   0.000     .3910982    .5326173
         _cons |  -4.060977   .5228107    -7.77   0.000    -5.085667   -3.036287
--------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
year: Identity               |
                   sd(_cons) |   1.790086   .1304536      1.551822    2.064931
------------------------------------------------------------------------------
LR test vs. logistic model: chibar2(01) = 14430.44    Prob >= chibar2 = 0.0000
(est2 stored)

. eststo:  xtmelogit atopally0 W_SystemRELa arep_MBallSW aiis_bl S I jointenemy_dum sqrtdist mpctdum poldif_using pol5_using || 
> year: W_SystemRELa  , intpoints(10)

Refining starting values: 

Iteration 0:   log likelihood = -30068.624  (not concave)
Iteration 1:   log likelihood = -30006.634  
Iteration 2:   log likelihood = -29967.797  

Performing gradient-based optimization: 

Iteration 0:   log likelihood = -29967.797  (not concave)
Iteration 1:   log likelihood = -29887.378  
Iteration 2:   log likelihood = -29856.971  (not concave)
Iteration 3:   log likelihood = -29691.224  (not concave)
Iteration 4:   log likelihood = -29608.133  (not concave)
Iteration 5:   log likelihood = -29547.358  
Iteration 6:   log likelihood = -29542.482  (not concave)
Iteration 7:   log likelihood =  -29542.42  
Iteration 8:   log likelihood = -29542.376  
Iteration 9:   log likelihood = -29542.376  

Mixed-effects logistic regression               Number of obs     =  1,045,707
Group variable: year                            Number of groups  =        184

                                                Obs per group:
                                                              min =        304
                                                              avg =    5,683.2
                                                              max =     25,078

Integration points =  10                        Wald chi2(10)     =   10068.70
Log likelihood = -29542.376                     Prob > chi2       =     0.0000

--------------------------------------------------------------------------------
     atopally0 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
  W_SystemRELa |  -9.217582   1.726966    -5.34   0.000    -12.60237   -5.832791
  arep_MBallSW |  -7.481771   .8236502    -9.08   0.000    -9.096095   -5.867446
       aiis_bl |  -3.612472   1.453243    -2.49   0.013    -6.460775   -.7641682
             S |   2.153046   .0889211    24.21   0.000     1.978764    2.327328
             I |   .4993048   .1395721     3.58   0.000     .2257486    .7728611
jointenemy_dum |   .6272014   .0315916    19.85   0.000      .565283    .6891198
      sqrtdist |   -.035637   .0005547   -64.25   0.000    -.0367241   -.0345499
       mpctdum |   .8183299   .0338692    24.16   0.000     .7519475    .8847123
  poldif_using |   -.014034   .0023792    -5.90   0.000    -.0186972   -.0093708
    pol5_using |   .4618158   .0361029    12.79   0.000     .3910554    .5325762
         _cons |  -4.030326   .5471585    -7.37   0.000    -5.102736   -2.957915
--------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
year: Independent            |
                sd(W_Syst~a) |   1.215743   3.246998      .0064783    228.1525
                   sd(_cons) |   1.754283   .2328997      1.352364    2.275651
------------------------------------------------------------------------------
LR test vs. logistic model: chi2(2) = 14428.63            Prob > chi2 = 0.0000

Note: LR test is conservative and provided only for reference.
(est3 stored)

. eststo:  xtmelogit atopally0 W_Regionalb  arep_MBallSW aiis_bl S I jointenemy_dum sqrtdist mpctdum poldif_using pol5_using || 
> year:                       , intpoints(10)

Refining starting values: 

Iteration 0:   log likelihood = -30024.839  (not concave)
Iteration 1:   log likelihood = -29866.764  
Iteration 2:   log likelihood = -29626.124  

Performing gradient-based optimization: 

Iteration 0:   log likelihood = -29626.124  
Iteration 1:   log likelihood =  -29588.62  
Iteration 2:   log likelihood = -29514.488  
Iteration 3:   log likelihood = -29509.758  
Iteration 4:   log likelihood = -29509.691  
Iteration 5:   log likelihood = -29509.691  

Mixed-effects logistic regression               Number of obs     =  1,045,707
Group variable: year                            Number of groups  =        184

                                                Obs per group:
                                                              min =        304
                                                              avg =    5,683.2
                                                              max =     25,078

Integration points =  10                        Wald chi2(10)     =   10042.26
Log likelihood = -29509.691                     Prob > chi2       =     0.0000

--------------------------------------------------------------------------------
     atopally0 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
   W_Regionalb |  -3.544246   .3637871    -9.74   0.000    -4.257255   -2.831236
  arep_MBallSW |  -7.316224   .8305698    -8.81   0.000    -8.944111   -5.688337
       aiis_bl |    -3.3989   1.429343    -2.38   0.017     -6.20036   -.5974387
             S |    2.26923   .0897671    25.28   0.000      2.09329    2.445171
             I |   .4496238   .1396514     3.22   0.001     .1759121    .7233355
jointenemy_dum |   .6016307   .0317283    18.96   0.000     .5394444     .663817
      sqrtdist |   -.035537   .0005593   -63.54   0.000    -.0366333   -.0344408
       mpctdum |   .2413073    .067074     3.60   0.000     .1098446      .37277
  poldif_using |    -.01295   .0023836    -5.43   0.000    -.0176219   -.0082782
    pol5_using |   .4859003   .0361887    13.43   0.000     .4149716    .5568289
         _cons |     -5.455   .2398831   -22.74   0.000    -5.925162   -4.984838
--------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
year: Identity               |
                   sd(_cons) |   1.836461   .1359363      1.588456    2.123188
------------------------------------------------------------------------------
LR test vs. logistic model: chibar2(01) = 13795.41    Prob >= chibar2 = 0.0000
(est4 stored)

. eststo:  xtmelogit atopally0 W_Regionalb  arep_MBallSW aiis_bl S I jointenemy_dum sqrtdist mpctdum poldif_using pol5_using || 
> year: W_Regionalb   , intpoints(10)

Refining starting values: 

Iteration 0:   log likelihood = -29875.546  (not concave)
Iteration 1:   log likelihood = -29755.886  
Iteration 2:   log likelihood = -29373.248  

Performing gradient-based optimization: 

Iteration 0:   log likelihood = -29373.248  
Iteration 1:   log likelihood = -29313.667  
Iteration 2:   log likelihood = -29220.596  
Iteration 3:   log likelihood = -29203.512  
Iteration 4:   log likelihood =  -29201.84  
Iteration 5:   log likelihood = -29201.824  
Iteration 6:   log likelihood = -29201.824  

Mixed-effects logistic regression               Number of obs     =  1,045,707
Group variable: year                            Number of groups  =        184

                                                Obs per group:
                                                              min =        304
                                                              avg =    5,683.2
                                                              max =     25,078

Integration points =  10                        Wald chi2(10)     =    9119.10
Log likelihood = -29201.824                     Prob > chi2       =     0.0000

--------------------------------------------------------------------------------
     atopally0 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
   W_Regionalb |  -10.11714   .9346223   -10.82   0.000    -11.94896   -8.285312
  arep_MBallSW |  -7.476482   .8395436    -8.91   0.000    -9.121957   -5.831006
       aiis_bl |  -3.742493   1.441366    -2.60   0.009    -6.567518   -.9174686
             S |   2.355774   .0909145    25.91   0.000     2.177585    2.533963
             I |     .47368   .1398619     3.39   0.001     .1995558    .7478042
jointenemy_dum |   .5976803   .0322115    18.55   0.000     .5345469    .6608136
      sqrtdist |  -.0356448   .0005638   -63.22   0.000    -.0367499   -.0345398
       mpctdum |  -.1399502   .0739432    -1.89   0.058    -.2848762    .0049759
  poldif_using |  -.0134748   .0023947    -5.63   0.000    -.0181683   -.0087814
    pol5_using |   .4905732   .0363317    13.50   0.000     .4193643     .561782
         _cons |   -3.62308   .3688118    -9.82   0.000    -4.345938   -2.900223
--------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
year: Independent            |
                sd(W_Reg~lb) |   7.007094   .6992303      5.762321    8.520762
                   sd(_cons) |    3.03204   .2796365      2.530644    3.632778
------------------------------------------------------------------------------
LR test vs. logistic model: chi2(2) = 14411.15            Prob > chi2 = 0.0000

Note: LR test is conservative and provided only for reference.
(est5 stored)

. 
. eststo:  xtmelogit atopally0              arep_MBallSW aiis_bl S I jointenemy_dum sqrtdist mpctdum poldif_using pol5_using || 
> year:              if polrel==1  , intpoints(10)

Refining starting values: 

Iteration 0:   log likelihood =  -11287.02  (not concave)
Iteration 1:   log likelihood = -11217.466  
Iteration 2:   log likelihood = -11156.714  

Performing gradient-based optimization: 

Iteration 0:   log likelihood = -11156.714  
Iteration 1:   log likelihood = -11128.747  
Iteration 2:   log likelihood = -11126.211  
Iteration 3:   log likelihood = -11126.184  
Iteration 4:   log likelihood = -11126.184  

Mixed-effects logistic regression               Number of obs     =    162,456
Group variable: year                            Number of groups  =        184

                                                Obs per group:
                                                              min =        176
                                                              avg =      882.9
                                                              max =      2,888

Integration points =  10                        Wald chi2(9)      =    1832.91
Log likelihood = -11126.184                     Prob > chi2       =     0.0000

--------------------------------------------------------------------------------
     atopally0 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
  arep_MBallSW |  -7.743543   1.346222    -5.75   0.000    -10.38209   -5.104997
       aiis_bl |  -2.142334   1.412138    -1.52   0.129    -4.910073    .6254059
             S |   1.947046    .139702    13.94   0.000     1.673235    2.220857
             I |  -.1266699   .1328632    -0.95   0.340     -.387077    .1337372
jointenemy_dum |   .6144619   .0441144    13.93   0.000     .5279994    .7009245
      sqrtdist |  -.0253554   .0009727   -26.07   0.000    -.0272619   -.0234488
       mpctdum |   .5449551   .0541611    10.06   0.000     .4388014    .6511088
  poldif_using |  -.0030736   .0038509    -0.80   0.425    -.0106213    .0044741
    pol5_using |   .3491325   .0564231     6.19   0.000     .2385453    .4597196
         _cons |  -6.688656   .2058679   -32.49   0.000    -7.092149   -6.285162
--------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
year: Identity               |
                   sd(_cons) |   1.754958   .1357398      1.508097    2.042228
------------------------------------------------------------------------------
LR test vs. logistic model: chibar2(01) = 4690.73     Prob >= chibar2 = 0.0000
(est6 stored)

. eststo:  xtmelogit atopally0 W_SystemRELa arep_MBallSW aiis_bl S I jointenemy_dum sqrtdist mpctdum poldif_using pol5_using || 
> year:              if polrel==1  , intpoints(10)

Refining starting values: 

Iteration 0:   log likelihood = -11241.678  (not concave)
Iteration 1:   log likelihood =  -11203.59  
Iteration 2:   log likelihood = -11123.386  

Performing gradient-based optimization: 

Iteration 0:   log likelihood = -11123.386  
Iteration 1:   log likelihood = -11112.591  
Iteration 2:   log likelihood = -11112.128  
Iteration 3:   log likelihood = -11112.126  
Iteration 4:   log likelihood = -11112.126  

Mixed-effects logistic regression               Number of obs     =    162,456
Group variable: year                            Number of groups  =        184

                                                Obs per group:
                                                              min =        176
                                                              avg =      882.9
                                                              max =      2,888

Integration points =  10                        Wald chi2(10)     =    1862.81
Log likelihood = -11112.126                     Prob > chi2       =     0.0000

--------------------------------------------------------------------------------
     atopally0 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
  W_SystemRELa |  -7.698196   1.483056    -5.19   0.000    -10.60493    -4.79146
  arep_MBallSW |  -7.707845   1.337995    -5.76   0.000    -10.33027   -5.085424
       aiis_bl |  -2.156808    1.41251    -1.53   0.127    -4.925277    .6116599
             S |    1.94162   .1395493    13.91   0.000     1.668108    2.215132
             I |  -.1249897   .1327812    -0.94   0.347    -.3852359    .1352566
jointenemy_dum |   .6100125   .0441057    13.83   0.000     .5235668    .6964581
      sqrtdist |  -.0254615   .0009736   -26.15   0.000    -.0273698   -.0235532
       mpctdum |   .5569156   .0541586    10.28   0.000     .4507666    .6630646
  poldif_using |  -.0034601   .0038466    -0.90   0.368    -.0109992    .0040791
    pol5_using |   .3378192   .0563663     5.99   0.000     .2273433    .4482951
         _cons |  -4.197859   .4998233    -8.40   0.000    -5.177495   -3.218223
--------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
year: Identity               |
                   sd(_cons) |   1.649159   .1259281      1.419926      1.9154
------------------------------------------------------------------------------
LR test vs. logistic model: chibar2(01) = 4455.35     Prob >= chibar2 = 0.0000
(est7 stored)

. eststo:  xtmelogit atopally0 W_SystemRELa arep_MBallSW aiis_bl S I jointenemy_dum sqrtdist mpctdum poldif_using pol5_using || 
> year: W_SystemRELa if polrel==1  , intpoints(10)

Refining starting values: 

Iteration 0:   log likelihood =  -11236.52  (not concave)
Iteration 1:   log likelihood = -11222.735  
Iteration 2:   log likelihood = -11201.018  

Performing gradient-based optimization: 

Iteration 0:   log likelihood = -11201.018  
Iteration 1:   log likelihood = -11181.056  (not concave)
Iteration 2:   log likelihood = -11159.194  
Iteration 3:   log likelihood = -11126.442  (not concave)
Iteration 4:   log likelihood = -11113.192  
Iteration 5:   log likelihood = -11112.512  
Iteration 6:   log likelihood = -11111.646  
Iteration 7:   log likelihood = -11111.643  
Iteration 8:   log likelihood = -11111.538  
Iteration 9:   log likelihood = -11111.536  
Iteration 10:  log likelihood = -11111.536  

Mixed-effects logistic regression               Number of obs     =    162,456
Group variable: year                            Number of groups  =        184

                                                Obs per group:
                                                              min =        176
                                                              avg =      882.9
                                                              max =      2,888

Integration points =  10                        Wald chi2(10)     =    1861.72
Log likelihood = -11111.536                     Prob > chi2       =     0.0000

--------------------------------------------------------------------------------
     atopally0 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
  W_SystemRELa |  -8.356584   1.657352    -5.04   0.000    -11.60493   -5.108234
  arep_MBallSW |  -7.688715   1.335227    -5.76   0.000    -10.30571   -5.071718
       aiis_bl |  -2.154566   1.412204    -1.53   0.127    -4.922435     .613303
             S |   1.940034    .139599    13.90   0.000     1.666425    2.213643
             I |  -.1248978    .132765    -0.94   0.347    -.3851124    .1353168
jointenemy_dum |   .6100119   .0441016    13.83   0.000     .5235742    .6964495
      sqrtdist |  -.0254673   .0009737   -26.16   0.000    -.0273757   -.0235589
       mpctdum |   .5572634   .0541567    10.29   0.000     .4511182    .6634085
  poldif_using |  -.0034821   .0038462    -0.91   0.365    -.0110206    .0040564
    pol5_using |   .3372607   .0563596     5.98   0.000     .2267979    .4477236
         _cons |  -4.043895   .5144773    -7.86   0.000    -5.052252   -3.035538
--------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
year: Independent            |
                sd(W_Syst~a) |   3.067565   1.502351      1.174667     8.01074
                   sd(_cons) |   1.376945   .2875177      .9144888    2.073264
------------------------------------------------------------------------------
LR test vs. logistic model: chi2(2) = 4456.53             Prob > chi2 = 0.0000

Note: LR test is conservative and provided only for reference.
(est8 stored)

. 
. esttab, b(a2) se(2) replace label star(* 0.10 ** 0.05 *** 0.01) scalars(N chi2  ll ) pr2(4) varwidth(25) modelwidth(9)  ///
> title(Table 1: Uncertainty and Alliance Formation, Multilevel Model)       ///
> nonumbers   ///
> mtitles("Model A1" "Model A2" "Model A3" "Model A4" "Model A5" "Model A6" "Model A7" "Model A8")  ///
> order(W_SystemRELa W_Regionalb  arep_MBallSW) ///
> nogap

Table 1: Uncertainty and Alliance Formation, Multilevel Model
--------------------------------------------------------------------------------------------------------------------------------
> -
                           Model A1     Model A2     Model A3     Model A4     Model A5     Model A6     Model A7     Model A8  
>  
--------------------------------------------------------------------------------------------------------------------------------
> -
eq1                                                                                                                             
>  
W_SystemRELa                               -9.09***     -9.22***                                            -7.70***     -8.36**
> *
                                          (1.59)       (1.73)                                              (1.48)       (1.66)  
>  
W_Regionalb                                                          -3.54***     -10.1***                                      
>  
                                                                    (0.36)       (0.93)                                         
>  
Alliance Reputation Sco~i     -7.50***     -7.48***     -7.48***     -7.32***     -7.48***     -7.74***     -7.71***     -7.69**
> *
                             (0.83)       (0.82)       (0.82)       (0.83)       (0.84)       (1.35)       (1.34)       (1.34)  
>  
Alliance Interstate Int~      -3.60**      -3.61**      -3.61**      -3.40**      -3.74***     -2.14        -2.16        -2.15  
>  
                             (1.45)       (1.45)       (1.45)       (1.43)       (1.44)       (1.41)       (1.41)       (1.41)  
>  
s score, unweighted, gl~p      2.16***      2.15***      2.15***      2.27***      2.36***      1.95***      1.94***      1.94**
> *
                             (0.09)       (0.09)       (0.09)       (0.09)       (0.09)       (0.14)       (0.14)       (0.14)  
>  
IIS_bl                         0.50***      0.50***      0.50***      0.45***      0.47***     -0.13        -0.12        -0.12  
>  
                             (0.14)       (0.14)       (0.14)       (0.14)       (0.14)       (0.13)       (0.13)       (0.13)  
>  
jointenemy_dum                 0.63***      0.63***      0.63***      0.60***      0.60***      0.61***      0.61***      0.61**
> *
                             (0.03)       (0.03)       (0.03)       (0.03)       (0.03)       (0.04)       (0.04)       (0.04)  
>  
distance                     -0.036***    -0.036***    -0.036***    -0.036***    -0.036***    -0.025***    -0.025***    -0.025**
> *
                             (0.00)       (0.00)       (0.00)       (0.00)       (0.00)       (0.00)       (0.00)       (0.00)  
>  
major power status             0.81***      0.82***      0.82***      0.24***     -0.14*        0.54***      0.56***      0.56**
> *
                             (0.03)       (0.03)       (0.03)       (0.07)       (0.07)       (0.05)       (0.05)       (0.05)  
>  
poldif_using                 -0.014***    -0.014***    -0.014***    -0.013***    -0.013***   -0.0031      -0.0035      -0.0035  
>  
                             (0.00)       (0.00)       (0.00)       (0.00)       (0.00)       (0.00)       (0.00)       (0.00)  
>  
pol5_using                     0.47***      0.46***      0.46***      0.49***      0.49***      0.35***      0.34***      0.34**
> *
                             (0.04)       (0.04)       (0.04)       (0.04)       (0.04)       (0.06)       (0.06)       (0.06)  
>  
Constant                      -7.01***     -4.06***     -4.03***     -5.45***     -3.62***     -6.69***     -4.20***     -4.04**
> *
                             (0.19)       (0.52)       (0.55)       (0.24)       (0.37)       (0.21)       (0.50)       (0.51)  
>  
--------------------------------------------------------------------------------------------------------------------------------
> -
lns1_1_1                                                                                                                        
>  
Constant                       0.66***      0.58***      0.20         0.61***      1.95***      0.56***      0.50***      1.12**
>  
                             (0.07)       (0.07)       (2.67)       (0.07)       (0.10)       (0.08)       (0.08)       (0.49)  
>  
--------------------------------------------------------------------------------------------------------------------------------
> -
lns1_1_2                                                                                                                        
>  
Constant                                                 0.56***                   1.11***                                0.32  
>  
                                                       (0.13)                    (0.09)                                 (0.21)  
>  
--------------------------------------------------------------------------------------------------------------------------------
> -
Observations                1045707      1045707      1045707      1045707      1045707       162456       162456       162456  
>  
Pseudo R-squared                                                                                                                
>  
chi2                        10036.2      10070.7      10068.7      10042.3       9119.1       1832.9       1862.8       1861.7  
>  
ll                         -29558.3     -29541.5     -29542.4     -29509.7     -29201.8     -11126.2     -11112.1     -11111.5  
>  
--------------------------------------------------------------------------------------------------------------------------------
> -
Standard errors in parentheses
* p<0.10, ** p<0.05, *** p<0.01

. 
. 
. *********************************************
. *APPENDIX TABLE 3**************************** - OK
. *********************************************
. clear

. use "K-adic Sample.dta"

. 
. *MODEL 1
. qui eststo clear

. qui eststo: logit defpact total_cinc total_cinc_square _prefail noallyrs _spline1 _spline2 _spline3

. qui eststo: logit defpact W_SystemRELa total_cinc total_cinc_square noallyrs _prefail _spline1 _spline2 _spline3

. 
. 
. esttab, b(a2) se(2) replace label star(* 0.10 ** 0.05 *** 0.01) scalars(N chi2  ll ) pr2(4) varwidth(25) modelwidth(9)  ///
> nonumbers   ///
> order(W_SystemRELa) ///
> addnote("") ///
> nogap

---------------------------------------------------
                            defpact      defpact   
---------------------------------------------------
defpact                                            
W_SystemRELa                               -4.10***
                                          (0.87)   
total_cinc                     8.70***      10.8***
                             (1.24)       (1.29)   
total_cinc_square             -14.6***     -17.6***
                             (2.77)       (2.80)   
# of previous failures        -1.35***     -1.46***
                             (0.20)       (0.21)   
noallyrs                      -0.13***     -0.11***
                             (0.04)       (0.04)   
(noallyrs-k1) cubed        -0.00076***  -0.00069***
                             (0.00)       (0.00)   
(noallyrs-k2) cubed         0.00036***   0.00032** 
                             (0.00)       (0.00)   
(noallyrs-k3) cubed       -0.000042**  -0.000035*  
                             (0.00)       (0.00)   
Constant                      -4.08***     -3.16***
                             (0.17)       (0.25)   
---------------------------------------------------
Observations                  24611        24611   
Pseudo R-squared             0.0432       0.0513   
chi2                          126.2        149.9   
ll                          -1396.7      -1384.9   
---------------------------------------------------
Standard errors in parentheses

* p<0.10, ** p<0.05, *** p<0.01

. 
. ******Predicted Probabilities from k-adic sample**
. capture drop `e(allsims)'

. set seed 12345

. estsimp logit defpact W_SystemRELa total_cinc total_cinc_square noallyrs _prefail _spline1 _spline2 _spline3

Iteration 0:   log likelihood = -1459.8189
Iteration 1:   log likelihood = -1399.1765
Iteration 2:   log likelihood =   -1385.62
Iteration 3:   log likelihood = -1384.8868
Iteration 4:   log likelihood = -1384.8796
Iteration 5:   log likelihood = -1384.8796

Logistic regression                               Number of obs   =      24611
                                                  LR chi2(8)      =     149.88
                                                  Prob > chi2     =     0.0000
Log likelihood = -1384.8796                       Pseudo R2       =     0.0513

------------------------------------------------------------------------------
     defpact |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
W_SystemRELa |  -4.098652   .8747871    -4.69   0.000    -5.813204   -2.384101
  total_cinc |   10.83131    1.28962     8.40   0.000     8.303701    13.35892
total_cinc~e |  -17.57426   2.801696    -6.27   0.000    -23.06549   -12.08304
    noallyrs |  -.1132368    .036916    -3.07   0.002    -.1855909   -.0408828
    _prefail |  -1.459415   .2058892    -7.09   0.000     -1.86295   -1.055879
    _spline1 |  -.0006853   .0002634    -2.60   0.009    -.0012016    -.000169
    _spline2 |   .0003209   .0001314     2.44   0.015     .0000635    .0005784
    _spline3 |  -.0000349   .0000193    -1.80   0.071    -.0000727    3.03e-06
       _cons |   -3.16484   .2540748   -12.46   0.000    -3.662817   -2.666862
------------------------------------------------------------------------------

Simulating main parameters.  Please wait....
% of simulations completed: 11% 22% 33% 44% 55% 66% 77% 88% 100% 

Number of simulations  : 1000
Names of new variables : b1 b2 b3 b4 b5 b6 b7 b8 b9

. setx total_cinc .0874678 total_cinc_square .00765062 noallyrs min _prefail 0 _spline1 0 _spline2 0 _spline3 0

. simqi, fd(prval(1)) changex(W_SystemRELa min max)

First Difference: W_SystemRELa min max

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
          dPr(defpact = 1) |  -.0307938     .0071981    -.0465161   -.0176473

. setx W_SystemRELa min

. simqi, prval(1)

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
             Pr(defpact=1) |   .0428838     .0072349     .0311681    .0585348

. setx W_SystemRELa max

. simqi, prval(1)

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
             Pr(defpact=1) |     .01209     .0031881     .0070861    .0195176

. 
. 
. 
. log close
      name:  <unnamed>
       log:  C:\Users\Dell\Dropbox\OutcomeUncertainty\II Replication Folder\Alliance Formation Log.log
  log type:  text
 closed on:  24 Sep 2017, 02:44:16
--------------------------------------------------------------------------------------------------------------------------------
